Using computer software to identify ore and waste in Alberta oil sands deposits
Bibliographic record
Abstract
Suncor Energy mines more than 900,000 tonnes of ore and oil sand tailings per day from the Steepbank and Millennium open pit mines in the Athabasca Oilsands near Fort McMurray, Alberta. A detailed understanding of the ore variation is necessary in order to process such high tonnage. The extraction process is very sensitive to grade distribution. A stratigraphic modeling technique was used to accurately delineate waste zones within the orebody. Modeling these deposits is challenging due to the stratigraphic deposition and the gradational grade distribution. The two-step process of developing a geologic model at Suncor Oilsands involved the creation of a stratigraphic model and the use of that model in the construction of a block model. The basic modeling steps were to recognize the depositional environment boundaries in each drill hole; identify zones of like facies, grade and processability type for correlation between drill holes within each depositional environment zone; and use the block model for ore zone analysis. Ore and waste zones are identified according to provincial regulatory definitions based on a combination of bitumen cutoff grade minimum mining thickness and separable waste thickness. Suncor's methodology to streamline the ore and waste identification process was described along with its application to categorize reserves and to generate mine design surfaces. 4 refs., 17 figs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".